在复杂的背景条件下改进了基于YOLOv8n的桥梁裂检测算法
Wenyuan Xu1, Hao Li1, Guodong Li1
1School of Civil Engineering and Transportation, Northeast Forestry University, Harbin, 150040, China.
Scientific reports
|April 16, 2025
概括
这项研究引入了一种改进的YOLOv8n模型,用于准确检测桥梁裂,显著减少错误检测和错误阳性. 改进的模型在识别密集裂和各种尺度方面表现出色,改善了结构完整性的评估.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 结构工程 结构工程
背景情况:
- 深度学习图像处理被广泛用于桥梁裂检测.
- 挑战包括由于照明,污点和密集的裂而导致的错误检测和错误阳性.
研究的目的:
- 提出一个改进的YOLOv8n模型,用于增强桥梁裂检测.
- 解决现有方法在检测具有挑战性的裂场景方面的局限性.
主要方法:
- 在脊椎和部内嵌入了全球注意力机制,以改进特征提取.
- 使用Gam-Concat优化了功能融合,并用Dysample替换了FPN-PAN上标样.
- 头部集成的MPDIoU损失,以完善密集裂的界限框回归.
主要成果:
- 与原始YOLOv8n模型相比,实现了mAP@0.5的3.02%和mAP@0.5:0.95的3.39%的增加.
- 在精度 (2.26%) 和回忆 (0.81%) 中显著改善.
- 在检测准确度方面表现优于其他比较型号,特别是在密集和不同规模的裂方面.
结论:
- 改进的YOLOv8n模型有效地提高了桥梁裂检测的准确性和稳定性.
- 拟议的改进为桥梁检查和结构健康监测提供了实际价值.
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